An HMM is a statistical model that generates observable sequences through a series of unobserved states. It can be visualized as a finite state machine that emits symbols as it progresses through states. HMMs have a strong statistical foundation and efficient learning algorithms, allowing them to handle inputs of variable length. They have been applied to problems like multiple sequence alignment, data mining, and modeling protein domains. However, HMMs also have limitations like many unstructured parameters, an inability to capture long-term dependencies, and not fully representing protein 3D structures.

1. By:
P.Joshna Rani
16031d7902

2. HIDDEN MARKOV MODEL:
HMM is called hidden because only the symbols emitted by
the system are observable, not the under lying random walk
between states.
An HMM can be visualized as a finite state machine. it
generates a protein sequence by emitting amino acids as it
progresses through a series of states.

3. ADVANTAGES:
 Strong statistical foundation
 Efficient learning algorithms-learning can take place directly from raw
sequence data.
 Allow consistent treatment of insertion and deletion penalties
in the form of locally learnable
 Can handle inputs of variable length-they are the most
flexible generalization of sequence profiles.
 Wide variety of applications including multiple alignment,
data mining and classification, structural analysis, and pattern
discovery.
 Can be combined into libraries.

4. DISADVANTAGES:
 HMMs often have a large number of unstructured parameters.
 First order HMMs are limited by their first-order markov property
 They cannot express dependencies between hidden states.
 Proteins fold into complex 3-D shapes determining their function.
 The HMM is unable to capture higher order correlation among
amino acids in a protein molecule.
 Only a small fraction of distributions over the space of possible
sequences can be represented by a reasonably constrained HMM.

5. APPLICATIONS:
Identification of G-protein coupled receptors
Clustering of paths for a subgroup
Gene prediction
Modeling protein domains

6. Thank you